{"id":"https://openalex.org/W4205560987","doi":"https://doi.org/10.1088/2632-2153/ac4d11","title":"BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale","display_name":"BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale","publication_year":2022,"publication_date":"2022-01-19","ids":{"openalex":"https://openalex.org/W4205560987","doi":"https://doi.org/10.1088/2632-2153/ac4d11"},"language":"en","primary_location":{"id":"doi:10.1088/2632-2153/ac4d11","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ac4d11","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ac4d11/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","datacite","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ac4d11/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034992311","display_name":"Carl Poelking","orcid":"https://orcid.org/0000-0001-8945-2027"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Carl Poelking","raw_affiliation_strings":["Astex Pharmaceuticals, Cambridge, United Kingdom","Department of Chemistry, University of Cambridge, Cambridge, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Astex Pharmaceuticals, Cambridge, United Kingdom","institution_ids":[]},{"raw_affiliation_string":"Department of Chemistry, University of Cambridge, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076518692","display_name":"Felix A. Faber","orcid":"https://orcid.org/0000-0002-3576-4137"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Felix A Faber","raw_affiliation_strings":["Department of Physics, University of Cambridge, Cambridge, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Department of Physics, University of Cambridge, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042914409","display_name":"Bingqing Cheng","orcid":"https://orcid.org/0000-0002-3584-9632"},"institutions":[{"id":"https://openalex.org/I157556583","display_name":"Institute of Science and Technology Austria","ror":"https://ror.org/03gnh5541","country_code":"AT","type":"education","lineage":["https://openalex.org/I157556583"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Bingqing Cheng","raw_affiliation_strings":["The Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria"],"affiliations":[{"raw_affiliation_string":"The Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria","institution_ids":["https://openalex.org/I157556583"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034992311","https://openalex.org/A5042914409"],"corresponding_institution_ids":["https://openalex.org/I157556583","https://openalex.org/I241749"],"apc_list":{"value":1600,"currency":"GBP","value_usd":1962},"apc_paid":{"value":1600,"currency":"GBP","value_usd":1962},"fwci":0.2262,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.39638204,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"3","issue":"4","first_page":"040501","last_page":"040501"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12613","display_name":"X-ray Diffraction in Crystallography","score":0.941100001335144,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.9536101818084717},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7452445030212402},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.600840151309967},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.574587345123291},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5697060823440552},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.490100234746933},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.47733622789382935},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45004189014434814},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.43030959367752075},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.42177021503448486},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3722379803657532},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.33542758226394653},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1508675217628479},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.11928072571754456},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07640382647514343}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.9536101818084717},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7452445030212402},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.600840151309967},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.574587345123291},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5697060823440552},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.490100234746933},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.47733622789382935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45004189014434814},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.43030959367752075},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42177021503448486},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3722379803657532},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.33542758226394653},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1508675217628479},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.11928072571754456},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07640382647514343},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1088/2632-2153/ac4d11","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ac4d11","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ac4d11/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},{"id":"pmh:oai:pub.research-explorer.ista.ac.at:12128","is_oa":true,"landing_page_url":"https://research-explorer.ista.ac.at/record/12128","pdf_url":null,"source":{"id":"https://openalex.org/S7407055285","display_name":"Institute of Science and Technology Austria","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Poelking C, Faber FA, Cheng B. BenchML: An extensible pipelining framework for benchmarking representations of materials and molecules at scale. <i>Machine Learning: Science and Technology</i>. 2022;3(4). doi:<a href=\"https://doi.org/10.1088/2632-2153/ac4d11\">10.1088/2632-2153/ac4d11</a>","raw_type":"http://purl.org/coar/resource_type/c_2df8fbb1"},{"id":"pmh:oai:www.repository.cam.ac.uk:1810/343381","is_oa":true,"landing_page_url":"https://www.repository.cam.ac.uk/handle/1810/343381","pdf_url":null,"source":{"id":"https://openalex.org/S4306401777","display_name":"Apollo (University of Cambridge)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I241749","host_organization_name":"University of Cambridge","host_organization_lineage":["https://openalex.org/I241749"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.17863/cam.90801","is_oa":true,"landing_page_url":"https://doi.org/10.17863/cam.90801","pdf_url":null,"source":{"id":"https://openalex.org/S7407050737","display_name":"Apollo","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.1088/2632-2153/ac4d11","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ac4d11","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ac4d11/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2465893628","display_name":"Peta-5: A National Facility for Petascale Data Intensive Computation and Analytics","funder_award_id":"EP/P020259/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G3330119427","display_name":null,"funder_award_id":"EPSRC","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4413028401","display_name":null,"funder_award_id":"EP/P020259/1.","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G5040371215","display_name":"Untersuchung der Wirkungsmechanismen von Rezepto- ren f\u00fcr Peptidwachstumsfaktoren und der damit ver- wandten Okogene.","funder_award_id":"20259","funder_id":"https://openalex.org/F4320320924","funder_display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung"},{"id":"https://openalex.org/G5101842829","display_name":null,"funder_award_id":"191736","funder_id":"https://openalex.org/F4320320924","funder_display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung"},{"id":"https://openalex.org/G5258663329","display_name":null,"funder_award_id":"EP/P020259/","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G5368029276","display_name":null,"funder_award_id":"EP/P020259/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G6661763476","display_name":null,"funder_award_id":"EP/P020259/1","funder_id":"https://openalex.org/F4320320273","funder_display_name":"University of Cambridge"},{"id":"https://openalex.org/G6703697151","display_name":null,"funder_award_id":"he Sustaining Innovation Program underthe Milner C","funder_id":"https://openalex.org/F4320314605","funder_display_name":"Astex Pharmaceuticals"},{"id":"https://openalex.org/G7772193777","display_name":null,"funder_award_id":"P2BSP2","funder_id":"https://openalex.org/F4320320924","funder_display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8719353587","display_name":null,"funder_award_id":"EP/P0","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320314605","display_name":"Astex Pharmaceuticals","ror":"https://ror.org/05fx1fs38"},{"id":"https://openalex.org/F4320320273","display_name":"University of Cambridge","ror":"https://ror.org/013meh722"},{"id":"https://openalex.org/F4320320924","display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung","ror":"https://ror.org/00yjd3n13"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4205560987.pdf","grobid_xml":"https://content.openalex.org/works/W4205560987.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W1975997599","https://openalex.org/W1988037271","https://openalex.org/W2029413789","https://openalex.org/W2033206800","https://openalex.org/W2033495141","https://openalex.org/W2066273100","https://openalex.org/W2076498053","https://openalex.org/W2104489082","https://openalex.org/W2114704115","https://openalex.org/W2119079616","https://openalex.org/W2157297264","https://openalex.org/W2158393027","https://openalex.org/W2312996603","https://openalex.org/W2319715725","https://openalex.org/W2337496963","https://openalex.org/W2560125654","https://openalex.org/W2601783526","https://openalex.org/W2620687153","https://openalex.org/W2728984672","https://openalex.org/W2761704992","https://openalex.org/W2766033847","https://openalex.org/W2785942661","https://openalex.org/W2800301423","https://openalex.org/W2810294304","https://openalex.org/W2900475422","https://openalex.org/W2900743800","https://openalex.org/W2910857709","https://openalex.org/W2911964244","https://openalex.org/W2912735973","https://openalex.org/W2937681869","https://openalex.org/W2964710575","https://openalex.org/W2971894235","https://openalex.org/W3036441178","https://openalex.org/W3041909131","https://openalex.org/W3049272068","https://openalex.org/W3049675384","https://openalex.org/W3089428833","https://openalex.org/W3094905049","https://openalex.org/W3099782627","https://openalex.org/W3101643101","https://openalex.org/W3101728438","https://openalex.org/W3102316735","https://openalex.org/W3120951562","https://openalex.org/W3132956480","https://openalex.org/W3141927472","https://openalex.org/W3165300194","https://openalex.org/W3178108585","https://openalex.org/W3185227028"],"related_works":["https://openalex.org/W4238897586","https://openalex.org/W435179959","https://openalex.org/W2619091065","https://openalex.org/W2059640416","https://openalex.org/W1490753184","https://openalex.org/W2284465472","https://openalex.org/W2291782699","https://openalex.org/W1993948687","https://openalex.org/W2011676020","https://openalex.org/W2077376691"],"abstract_inverted_index":{"Abstract":[0],"We":[1],"introduce":[2],"a":[3,85,92,100],"machine-learning":[4],"(ML)":[5],"framework":[6],"for":[7,49],"high-throughput":[8],"benchmarking":[9,27],"of":[10,13,18,62,95,103,116],"diverse":[11,101],"representations":[12,118],"chemical":[14],"systems":[15],"against":[16],"datasets":[17],"materials":[19],"and":[20,53,106],"molecules.":[21],"The":[22,66],"guiding":[23],"principle":[24],"underlying":[25],"the":[26,96,113],"approach":[28],"is":[29],"to":[30,39,81],"evaluate":[31],"raw":[32],"descriptor":[33],"performance":[34],"by":[35],"limiting":[36],"model":[37],"complexity":[38],"simple":[40],"regression":[41],"schemes":[42],"while":[43],"enforcing":[44],"best":[45],"ML":[46],"practices,":[47],"allowing":[48],"unbiased":[50],"hyperparameter":[51],"optimization,":[52],"assessing":[54],"learning":[55,58],"progress":[56],"through":[57],"curves":[59],"along":[60],"series":[61],"synchronized":[63],"train-test":[64],"splits.":[65],"resulting":[67],"models":[68],"are":[69],"intended":[70],"as":[71,119,121],"baselines":[72],"that":[73],"can":[74,88],"inform":[75],"future":[76],"method":[77],"development,":[78],"in":[79],"addition":[80],"indicating":[82],"how":[83],"easily":[84],"given":[86],"dataset":[87],"be":[89],"learnt.":[90],"Through":[91],"comparative":[93],"analysis":[94],"training":[97],"outcome":[98],"across":[99],"set":[102],"physicochemical,":[104],"topological":[105],"geometric":[107],"representations,":[108],"we":[109],"glean":[110],"insight":[111],"into":[112],"relative":[114],"merits":[115],"these":[117],"well":[120],"their":[122],"interrelatedness.":[123]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2022-10-07T00:00:00"}
